A Code Implementation to Build an AI-Powered PDF Interaction System in Google Colab Using Gemini Flash 1.5, PyMuPDF, and Google Generative AI API

In this tutorial, we explain how to create a PDF interactive system that works with the Google Colab using Gemini Flash 1.5, PYMUPDF and Google General . By taking advantage of these tools, we can download PDF smoothly, extract their text, ask questions interactively, and receive smart responses from the latest Gemini Flash 1.5.

!pip install -q -U google-generativeai PyMuPDF python-dotenv

First, we install the consequences to build the PDF Q&A system that works in the Google Colab. Google-Henerativeai provides access to Gemini Flash 1.5, allowing natural language reactions, while Pymupdf (also known as Fitz) allows the effective text of PDFS. Also, Python-Dotenv helps manage environmental variables, such as API keys, safely inside the notebook.

from google.colab import files
uploaded = files.upload()

We download files from your local device to Google Colab. When implementing, the file of the file selecting the file opens, allowing you to choose a file (for example, PDF) for download. The downloaded file is stored in a dictionary -like object (downloaded), where the keys represent file names and values ​​that contain the binary data of the file. This step is necessary to process documents, data collections, or typical weights directly in the Colab environment.

import fitz


def extract_pdf_text(pdf_path):
    doc = fitz.open(pdf_path)
    full_text = ""
    for page in doc:
        full_text += page.get_text()
    return full_text


pdf_file_path="/content/Paper.pdf"
document_text = extract_pdf_text(pdf_path=pdf_file_path)
print("Document text extracted!")
print(document_text[:1000]) 

We use PYMUPDF (FITZ) to extract the text from the PDF file in Google Colab. You read the Extract_Pdf_text (pdf_path) pdf, and it is repeated through its pages, and the text content recovers. Then the extracted text is stored in the document _ Text, with the first 1000 characters printing to preview the content. This step is crucial to enable the analysis based on the text and answer AI questions from PDF.

import os
os.environ["GOOGLE_API_KEY"] = 'Use your own API key here'

We set the Google API key as an environmentally variable in Google Colab. The API key is required to ratify the requests to Google Generative AI, allowing to reach Gemini Flash 1.5 to handle the Acting texts. The replacement of the “use of your API key here” ensures a valid key that the model can create responses safely inside the notebook.

import google.generativeai as genai


genai.configure(api_key=os.environ["GOOGLE_API_KEY"])


model_name = "models/gemini-1.5-flash-001"


def query_gemini_flash(question, context):
    model = genai.GenerativeModel(model_name=model_name)
    prompt = f"""
Context: {context[:20000]}


Question: {question}


Answer:
"""
    response = model.generate_content(prompt)
    return response.text


pdf_text = extract_pdf_text("/content/Paper.pdf")


question = "Summarize the key findings of this document."
answer = query_gemini_flash(question, pdf_text)
print("Gemini Flash Answer:")
print(answer)

Finally, we are composed and Speque Gemini Flash 1.5 using a PDF document to generate the text in which artificial intelligence works. It creates the Genai Library using the API key and downloads the Gemini Flash 1.5 (GEINI-1.5-Flash -001). Query_gemini_flash () takes a question and extracts the extracted PDF text as inputs, places an organized mentor, and recovers a response created by artificial intelligence. This setting allows the automatic document, questions and smart answers from PDFS.

In conclusion, after this tutorial, we have successfully built an interactive interactive system based on the PDF in Google Colab using Gemini Flash 1.5, PYMUPDF and Google Generative Ai API. This solution enables users to extract information from PDFS and inquire about it easily. A mixture of Google’s advanced artificial intelligence and the COLAB environment provides a strong and accessible way to address large documents without the need for heavy calculations.

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